Link label prediction is the problem of predicting the missing labels or signs of all the unlabeled edges in a network. For signed networks, these labels can either be positive or negative. In recent years, different algorithms have been proposed such as using regression, trust propagation and matrix factorization. These approaches have tried to solve the problem of link label prediction by using ideas from social theories, where most of them predict a single missing label given that labels of other edges are known. However, in most real-world social graphs, the number of labeled edges is usually less than that of unlabeled edges. Therefore, predicting a single edge label at a time would require multiple runs and is more computationally demanding. In this thesis, we look at link label prediction problem on a signed citation network with missing edge labels. Our citation network consists of papers from three major machine learning and data mining conferences together with their references, and edges showing the relationship between them. An edge in our network is labeled either positive (dataset relevant) if the reference is based on the dataset used in the paper or negative otherwise. We present three approaches to predict the missing labels. The first approach converts the label prediction problem into a standard classification problem. We then, generate a set of features for each edge and then adopt Support Vector Machines in solving the classification problem. For the second approach, we formalize the graph such that the edges are represented as nodes with links showing similarities between them. We then adopt a label propagation method to propagate the labels on known nodes to those with unknown labels. In the third approach, we adopt a PageRank approach where we rank the nodes according to the number of incoming positive and negative edges, after which we set a threshold. Based on the ranks, we can infer an edge would be positive if it goes a node above the threshold. Experimental results on our citation network corroborate the efficacy of these approaches. With each edge having a label, we also performed additional network analysis where we extracted a subnetwork of the dataset relevant edges and nodes in our citation network, and then detected different communities from this extracted sub-network. To understand the different detected communities, we performed a case study on several dataset communities. The study shows a relationship between the major topic areas in a dataset community and the data sources in the community.

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Den stora mängden tillgänglig data på internet kan användas för att förbättra förutsägelser genom maskininlärning. Problemet är att sådan data ofta är i ett obehandlat format och kräver att någon manuellt bestämmer etiketter på den insamlade datan innan den kan användas av algoritmen. Semi-supervised learning (SSL) är en teknik där algoritmen använder ett fåtal förbehandlade exempel och därefter automatiskt bestämmer etiketter för resterande data. Ett tillvägagångssätt inom SSL är att representera datan i en graf, vilket kallas för graf-baserad semi-supervised learning (GSSL), och sedan hitta likheter mellan noderna i grafen för att automatiskt bestämma etiketter.Vårt mål i denna uppsatsen är att förenkla de avancerade processerna och stegen för att implementera en GSSL-algoritm. Vi kommer att gå igen grundläggande steg som hur utvecklingsmiljön ska installeras men även mer avancerade steg som data pre-processering och feature extraction. Feature extraction metoderna som uppsatsen använder sig av är bag-of-words (BOW) och term frequency-inverse document frequency (TF-IDF). Slutgiltligen presenterar vi klassificering av dokument med Label Propagation (LP) och Multinomial Naive Bayes (MNB) samt en detaljerad beskrivning över hur GSSL fungerar.Vi presenterar även prestanda för klassificering-algoritmerna genom att klassificera 20 Newsgroup datasetet med LP och MNB. Resultaten dokumenteras genom två olika utvärderingspoäng vilka är F1-score och accuracy. Vi gör även en jämförelse mellan MNB och LP med två olika typer av kärnor, KNN och RBF, på olika mängder av förbehandlade träningsdokument. Resultaten ifrån klassificering-algoritmerna visar att MNB är bättre på att klassificera datasetet än LP. / The large amount of available data on the web can be used to improve the predictions made by machine learning algorithms. The problem is that such data is often in a raw format and needs to be manually labeled by a human before it can be used by a machine learning algorithm. Semi-supervised learning (SSL) is a technique where the algorithm uses a few prepared samples to automatically prepare the rest of the data. One approach to SSL is to represent the data in a graph, also called graph-based semi-supervised learning (GSSL), and find similarities between the nodes for automatic labeling.Our goal in this thesis is to simplify the advanced processes and steps to implement a GSSL-algorithm. We will cover basic tasks such as setup of the developing environment and more advanced steps such as data preprocessing and feature extraction. The feature extraction techniques covered are bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF). Lastly, we present how to classify documents using Label Propagation (LP) and Multinomial Naive Bayes (MNB) with a detailed explanation of the inner workings of GSSL. We showcased the classification performance by classifying documents from the 20 Newsgroup dataset using LP and MNB. The results are documented using two different evaluation scores called F1-score and accuracy. A comparison between MNB and the LP-algorithm using two different types of kernels, KNN and RBF, was made on different amount of labeled documents. The results from the classification algorithms shows that MNB is better at classifying the data than LP.

[pt] O aprendizado supervisionado é, hoje, o ramo do aprendizado de máquina central para a maioria das inovações nos negócios. A abordagem depende de ter grandes quantidades de dados rotulados, suficiente para ajustar funções com a precisão necessária. No entanto, pode ser caro obter dados rotulados ou criar os rótulos através de um processo de anotação. O aprendizado semisupervisionado (SSL) é usado para rotular com precisão os dados a partir de pequenas quantidades de dados rotulados utilizando técnicas de aprendizado não supervisionado. Uma técnica de rotulagem é a propagação de rótulos. Neste trabalho, usamos especificamente o algoritmo Consensus rate-based label propagation (CRLP). Este algoritmo depende do uma função de consenso para a propagação. Uma possível função de consenso é a matriz de co-associação que estima a probabilidade dos pontos i e j pertencem ao mesmo grupo. Neste trabalho, observamos que a matriz de co-associação contém informações valiosas para tratar esse tipo de problema. Quando nenhum dado está rotulado, é comum escolher aleatoriamente, com probabilidade uniforme, os dados a serem rotulados manualmente, a partir dos quais a propagação procede. Este trabalho aborda o problema de seleção de um conjunto de tamanho fixo de dados para serem rotulados manualmente que propiciem uma melhor precisão no algoritmo de propagação de rótulos. Três técnicas de seleção, baseadas em princípios de amostragem estocástica, são propostas: Stratified Sampling (SS), Probability (P), and Stratified Sampling - Probability (SSP). Eles são todos baseados nas informações embutidas na matriz de co-associação. Os experimentos foram realizados em 15 conjuntos de benchmarks e mostraram resultados muito interessantes. Não só, porque eles fornecem uma seleção mais equilibrada quando comparados a uma seleção aleatória, mas também melhoram os resultados de precisão na propagação de rótulos. Em outro contexto, essas estratégias também foram testadas dentro de um processo de aprendizagem ativa, obtendo também bons resultados. / [en] Supervised learning is, today, the branch of Machine Learning central to most business disruption. The approach relies on having amounts of labeled data large enough to learn functions with the required approximation. However, labeled data may be expensive, to obtain or to construct through a labeling process. Semi-supervised learning (SSL) strives to label accurately data from small amounts of labeled data and the use of unsupervised learning techniques. One labeling technique is label propagation. We use specifically the Consensus rate-based label propagation (CRLP) in this work. A consensus function is central to the propagation. A possible consensus function is a coassociation matrix that estimates the probability of data points i and j belong to the same group. In this work, we observe that the co-association matrix has valuable information embedded in it. When no data is labeled, it is common to choose with a uniform probability randomly, the data to manually label, from which the propagation proceeds. This work addresses the problem of selecting a fixed-size set of data points to label (manually), to improve the label propagation algorithm s accuracy. Three selection techniques, based on stochastic sampling principles, are proposed: Stratified Sampling (SP), Probability (P), and Stratified Sampling - Probability (SSP). They are all based on the information embedded in the co-association matrix. Experiments were carried out on 15 benchmark sets and showed exciting results. Not only because they provide a more balanced selection when compared to a uniform random selection, but also improved the accuracy results of a label propagation method. These strategies were also tested inside an active learning process in a different context, also achieving good results.

Artificial Intelligence in Automotive Industry / Cost reduction is one of the main concerns in industry. Companies invest considerably for better performance in end-of-line fault diagnosis systems. A common strategy is to use data obtained from existing instrumentation. This research investigates the challenge of learning from historical data that have already been collected by companies. Machine learning is basically one of the most common and powerful techniques of artificial intelligence that can learn from data and identify fault features with no need for human interaction. In this research, labeled sound and vibration measurements are processed into fault signatures for vehicle starter motors and alternators. A fault detection and identification system has been developed to identify fault types for end-of-line testing of motors. However, labels are relatively difficult to obtain, expensive, time consuming and require experienced humans, while unlabeled samples needs less effort to collect. Thus, learning from unlabeled data together with the guidance of few labels would be a better solution. Furthermore, in this research, learning from unlabeled data with absolutely no human intervention is also implemented and discussed as well. / Thesis / Master of Applied Science (MASc)